Ms. Mingyuan Zhu Profile

Mingyuan Zhu

at Northeast Petroleum Univ

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Proceedings Article | 12 September 2021 Poster + Presentation + Paper

Detection of industrial storage tanks at the city-level from optical satellite remote sensing images

Mingyuan Zhu, Zhibao Wang, Lu Bai, Jie Zhang, Jinhua Tao, Liangfu Chen

Proceedings Volume 11862, 118620Y (2021) https://doi.org/10.1117/12.2600008

KEYWORDS: Remote sensing, Detection and tracking algorithms, Hough transforms, Satellites, Satellite imaging, Earth observing sensors, Data modeling, Inspection, Image processing, Target detection

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Industrial storage tanks are widely used in petroleum, chemical industry, metallurgy and other process industries. The use of storage tanks are important in ensuring industrial safety production and product storage. Recently, concerns are rising over leaks and spills which leads to potential environmental and public health risks, for example, air pollution. Thus, there is a need to detect and monitor the tanks, which has become a regulatory requirement in most countries. The monitoring of industrial storage tanks in a city is of great practical significance to the planning and construction of the city. With the recent development of remote sensing technology, computer vision and image processing, it is possible to automatically detect industrial storage tanks from optical remote sensing images. This paper mainly studies the automatic detection of urban industrial storage tanks using deep learning based algorithm, aiming to help people manage and monitor the urban environment and resources. In this paper, we collected optical satellite remote sensing images of the city as a unit and created a city-level dataset including three cities of Guangdong province in south China by utilising satellite image data obtained from Google Earth imagery. To explore the effect of the deep learning object detection algorithm for the industrial storage tanks detection at the city-level, a deep learning model built with SSD (Single-Shot Detector) was trained based on our collected dataset. To further improve the detection accuracy of industrial storage tanks, Hough transform was used to reduce the false alarm in the deep learning results. The experimental results show that the combination of deep learning target detection model and Hough transform is effective in detecting of industrial storage tank on the collected city-level dataset and can achieve promising detection results.

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